ArticleScientific reports2021
Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- "Several birds with one stone": exploring the potential of AI methods for multi-target drug design.Molecular diversity · 2025Article
- VenomPred 2.0: A NovelJournal of chemical information and modeling · 2024Article
- Chemical and biological language models in molecular design: opportunities, risks and scientific reasoning.Future science OA · 2024Article
- Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values.Molecules (Basel, Switzerland) · 2023Article
- Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual Targets.Molecules (Basel, Switzerland) · 2023Article
- Advances in Computational Polypharmacology.Molecular informatics · 2022Review
- Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature Analysis.Biomolecules · 2022Article
- Explainable machine learning predictions of dual-target compounds reveal characteristic structural features.Scientific reports · 2021Article
- Multiomics Identification of Potential Targets for Alzheimer Disease and Antrocin as a Therapeutic Candidate.Pharmaceutics · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compounds with defined multi-target activity (promiscuity) play an increasingly important role in drug discovery. However, the molecular basis of multi-target activity is currently only little understood. In particular, it remains unclear whether structural features exist that generally characterize promiscuous compounds and set them apart from compounds with single-target activity. We have devised a test system using machine learning to systematically examine structural features that might characterize compounds with multi-target activity. Using this system, more than 860,000 diagnostic predictions were carried out. The analysis provided compelling evidence for the presence of structural characteristics of promiscuous compounds that were dependent on given target combinations, but not generalizable. Feature weighting and mapping identified characteristic substructures in test compounds. Taken together, these findings are relevant for the design of compounds with desired multi-target activity.
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Registered trials
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